ReviewFrontiers in digital health2026
Artificial intelligence and digital health equity: a post-pandemic evidence synthesis and implementation safeguards framework.
Review in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
1 author.
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Abstract
Introduction: The rapid expansion of AI-enabled digital health after COVID-19 created new possibilities for extending care while raising concerns about unequal access, subgroup underperformance, and weak accountability. These effects remain difficult to interpret because telehealth infrastructure, predictive analytics, clinical decision support, and generative AI operate through different equity mechanisms. Methods: A transparent narrative evidence synthesis was conducted using peer-reviewed literature and selected governance documents. Structured searches of PubMed, Scopus, Web of Science, and IEEE Xplore were refreshed on March 5, 2026, for English-language sources published between January 1, 2020, and December 31, 2025. A second reviewer independently assessed a sample of 12 full-text inclusion and exclusion decisions using the same pre-specified criteria, with 100% agreement. The final corpus included 29 sources. Results: Evidence was strongest for digital access barriers, subgroup underperformance, and implementation-related governance concerns. AI-assisted screening and digitally mediated remote support showed conditional benefits in targeted settings when high-touch support was built into implementation. Evidence on generative AI and language-support tools was comparatively smaller, newer, and concentrated in representational-bias applications, with limited evidence of sustained downstream equity gains. Conclusion: AI-enabled digital health may reduce selected barriers to care when supported by equitable access conditions, subgroup validation, accountable governance, and implementation oversight. Equity gains were conditional rather than automatic, and evidentiary depth varied substantially across modalities. The review distills recurring patterns into a synthesis-derived operational roadmap for procurement, validation, implementation, and post-deployment monitoring.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.